AI-ACCELERATED DRUG DISCOVERY
Available from Reaxense
This protein is integrated into the Receptor.AI ecosystem as a prospective target with high therapeutic potential. We performed a comprehensive characterization of 3-hydroxyacyl-CoA dehydrogenase type-2 including:
1. LLM-powered literature research
Our custom-tailored LLM extracted and formalized all relevant information about the protein from a large set of structured and unstructured data sources and stored it in the form of a Knowledge Graph. This comprehensive analysis allowed us to gain insight into 3-hydroxyacyl-CoA dehydrogenase type-2 therapeutic significance, existing small molecule ligands, relevant off-targets, and protein-protein interactions.
Fig. 1. Preliminary target research workflow
2. AI-Driven Conformational Ensemble Generation
Starting from the initial protein structure, we employed advanced AI algorithms to predict alternative functional states of 3-hydroxyacyl-CoA dehydrogenase type-2, including large-scale conformational changes along "soft" collective coordinates. Through molecular simulations with AI-enhanced sampling and trajectory clustering, we explored the broad conformational space of the protein and identified its representative structures. Utilizing diffusion-based AI models and active learning AutoML, we generated a statistically robust ensemble of equilibrium protein conformations that capture the receptor's full dynamic behavior, providing a robust foundation for accurate structure-based drug design.
Fig. 2. AI-powered molecular dynamics simulations workflow
3. Binding pockets identification and characterization
We employed the AI-based pocket prediction module to discover orthosteric, allosteric, hidden, and cryptic binding pockets on the protein’s surface. Our technique integrates the LLM-driven literature search and structure-aware ensemble-based pocket detection algorithm that utilizes previously established protein dynamics. Tentative pockets are then subject to AI scoring and ranking with simultaneous detection of false positives. In the final step, the AI model assesses the druggability of each pocket enabling a comprehensive selection of the most promising pockets for further targeting.
Fig. 3. AI-based binding pocket detection workflow
4. AI-Powered Virtual Screening
Our ecosystem is equipped to perform AI-driven virtual screening on 3-hydroxyacyl-CoA dehydrogenase type-2. With access to a vast chemical space and cutting-edge AI docking algorithms, we can rapidly and reliably predict the most promising, novel, diverse, potent, and safe small molecule ligands of 3-hydroxyacyl-CoA dehydrogenase type-2. This approach allows us to achieve an excellent hit rate and to identify compounds ready for advanced lead discovery and optimization.
Fig. 4. The screening workflow of Receptor.AI
Receptor.AI, in partnership with Reaxense, developed a next-generation technology for on-demand focused library design to enable extensive target exploration.
The focused library for 3-hydroxyacyl-CoA dehydrogenase type-2 includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.
3-hydroxyacyl-CoA dehydrogenase type-2
partner:
Reaxense
upacc:
Q99714
UPID:
HCD2_HUMAN
Alternative names:
17-beta-estradiol 17-dehydrogenase; 2-methyl-3-hydroxybutyryl-CoA dehydrogenase; 3-alpha-(17-beta)-hydroxysteroid dehydrogenase (NAD(+)); 3-hydroxy-2-methylbutyryl-CoA dehydrogenase; 3-hydroxyacyl-CoA dehydrogenase type II; 3alpha(or 20beta)-hydroxysteroid dehydrogenase; 7-alpha-hydroxysteroid dehydrogenase; Endoplasmic reticulum-associated amyloid beta-peptide-binding protein; Mitochondrial ribonuclease P protein 2; Short chain dehydrogenase/reductase family 5C member 1; Short-chain type dehydrogenase/reductase XH98G2; Type II HADH
Alternative UPACC:
Q99714; Q5H927; Q6IBS9; Q8TCV9; Q96HD5
Background:
3-hydroxyacyl-CoA dehydrogenase type-2 (HSD17B10) is a multifunctional mitochondrial enzyme involved in fatty acid, branched-chain amino acid, and steroid metabolism. It plays a critical role in mitochondrial fatty acid beta-oxidation, isoleucine degradation, and the metabolism of various steroids. Additionally, it exhibits phospholipase C-like activity and may protect cells from apoptosis during oxidative stress. HSD17B10 also interacts with amyloid-beta, potentially contributing to Alzheimer's disease pathology.
Therapeutic significance:
HSD10 mitochondrial disease, linked to HSD17B10, manifests with neurodegeneration, psychomotor retardation, and metabolic acidosis. Understanding HSD17B10's role could unveil novel therapeutic strategies for this disease and conditions involving metabolic dysregulation.
